Cell-specific DNA methylation in human alpha and beta cells regulates gene expression in type 2 diabetes

Ofori, Jones K.; Ruhrmann, Sabrina; Lindström, Axel; Perfilyev, Alexander; Martin, Melina; Karagiannopoulos, Alexandros; Scisciola, Lucia; Kost, Katja; Jönsson, Josefine; Nilsson, Åsa; Kantor, Boris; Dudenhöffer-Pfeifer, Monika; Rots, Marianne G.; Wendt, Anna; Rönn, Tina; Eliasson, Lena; Bacos, Karl; Ling, Charlotte

doi:10.1038/s42255-026-01498-9

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Published: 24 April 2026

Cell-specific DNA methylation in human alpha and beta cells regulates gene expression in type 2 diabetes

Nature Metabolism volume 8, pages 957–980 (2026)Cite this article

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Abstract

Epigenome-wide studies of pancreatic islets provide valuable insights into type 2 diabetes (T2D) but lack methylomes from individual cell types. Here we show changes to alpha and beta cell-specific methylomes and transcriptomes from people with or without T2D, using whole-genome bisulfite sequencing and RNA sequencing. We discover 22,544 differentially methylated regions annotated to 7,975 genes in alpha versus beta cells, such as INS, GCG, PDX1 and PCSK1, with ~50% showing differential expression. CRISPR–dCas9–DNMT3A-based epigenetic editing increases INS and TH DNA methylation, while CRISPR–dCas9–TET1-based editing decreases GCG methylation, each altering INS, TH or GCG expression and content in beta cells. Pre-T2D/T2D-associated differentially methylated regions in alpha and beta cells overlap 12–18% of T2D-associated genome-wide association study candidates. Additionally, ONECUT2 is epigenetically upregulated in beta cells from people with pre-T2D/T2D and elevated in male Goto-Kakizaki rat islets. ONECUT2 overexpression in beta cells/islets downregulates gene sets impacting insulin secretion and glucose homeostasis, and reduces mitochondrial activity, ATP/ADP ratio and insulin secretion. We also provide ‘alpha-beta-methylome’ (https://alpha-beta-methylome.serve.scilifelab.se/app/alpha-beta-methylome/), a resource exploring T2D, age and sex associations on methylation, highlighting cell-specific epigenetic regulation and dysfunctions contributing to T2D.

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Main

T2D, characterized by hyperglycaemia driven by impaired insulin secretion from pancreatic islets and peripheral insulin resistance^1,2, is rapidly increasing worldwide³. Pancreatic islets contain several cell types controlling glucose homeostasis. In the fasted state, alpha cells secrete glucagon to raise plasma glucose levels, while in the fed state, beta cells secrete insulin to decrease glucose levels. Impaired insulin secretion is required for T2D development, but excess glucagon also contributes to hyperglycaemia^1,4.

Genome-wide association studies (GWAS) identified single-nucleotide polymorphisms (SNPs) linked to T2D and islet dysfunction². Additionally, epigenetic mechanisms, linking the environment and genome, are of importance for T2D^5,6. Indeed, earlier studies discovered alterations in DNA methylation in pancreatic islets, liver, adipose tissue and muscle from people with T2D versus controls^{7,8,9,10,11,12,13,14,15,16,17,18,19}. However, prior studies analysed methylomes in whole tissues, containing different cell types, although it is established that epigenetics controls cell-type-specific gene expression. Hence, studies focusing on DNA methylation in individual cell types in tissues from people with T2D are needed. To our knowledge, a few studies have analysed cell-specific methylomes in humans²⁰, but none were done in tissues from people with T2D. A better understanding of epigenetic and molecular defects in individual cell types in islets from people with or at risk of T2D could identify previously unrecognized aetiopathogenetic mechanisms and drug targets. Additionally, studies exploring whether altering DNA methylation in regulatory regions of alpha and beta cell-specific genes causes expression changes and alters hormone secretion are desirable.

Single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) can identify transcriptome and open chromatin differences in individual cell types of a tissue²¹. However, most single-cell studies are based on smaller numbers of cells from each tissue, and individual, thus representing only minute parts of a tissue. scRNA-seq studies identified differentially expressed genes (DEGs) in islets from donors with T2D versus controls^21,22. The DEGs identified in these studies are largely non-overlapping, potentially because many scRNA-seq protocols capture only 5–40% of transcripts in a cell, have low power due to analysis of smaller cohorts, and/or are affected by selection biases for certain cell types²². Methods capturing more transcripts from each cell, and more individual cells from each donor, may resolve these issues.

To fill these knowledge gaps, we used Method for Analyzing RNA following Intracellular Sorting (MARIS)²³ to sort larger numbers (~300,000 per donor) of alpha and beta cells from human pancreatic islets of donors with or without T2D, followed by whole-genome bisulfite sequencing (WGBS), the most comprehensive method to study methylation genome wide, and RNA-seq. Firstly, we aimed to characterize the global methylome and transcriptome of human alpha and beta cells and use epigenetic editing to investigate causative impacts of DNA methylation on transcription of alpha and beta cell-specific genes. Secondly, we aimed to identify previously unrecognized candidate genes contributing to impaired insulin or glucagon secretion by studying the impact of pre-T2D/T2D on methylomes and transcriptomes in human alpha and beta cells, followed by functional validation by manipulation of identified T2D-associated candidates in human islets and cell lines together with analyses of diabetic Goto-Kakizaki (GK) rats. The methylomes in alpha and beta cells were also integrated with transcription factor (TF) binding²⁴ and T2D genetics². Finally, we developed the web tool alpha-beta-methylome (https://alpha-beta-methylome.serve.scilifelab.se/app/alpha-beta-methylome/), a comprehensive resource for investigating the impact of age, sex and T2D on DNA methylation and gene expression in human alpha and beta cells.

Results

WGBS and RNA-seq of human alpha and beta cells

Using MARIS for cell sorting of human islets²³, we obtained on average 322,627 ± 73,055 alpha cells and 311,550 ± 61,250 beta cells from 24 donors with or without T2D (Fig. 1a and Extended Data Fig. 1a). Characteristics of all donors in this study (17 non-diabetic controls and 7 pre-T2D/T2D) and donors for each analysis are presented in Supplementary Table 1. WGBS analysed genome-wide DNA methylation at base-pair resolution on alpha and beta cell samples where we had enough DNA (Fig. 1b). Supplementary Table 2 reports sequencing information for WGBS data passing quality control (n = 10, alpha cells; n = 14, beta cells). After deduplication, ~84% of reads mapped uniquely to GRCh38. After quality control, the transcriptome was analysed by RNA-seq in alpha and beta cells from 22 donors (Fig. 1b and Supplementary Table 1b).

**Fig. 1: Analysis of global DNA methylome in human alpha and beta cells.**

Global methylome of human alpha and beta cells

We next characterized the global methylome in alpha and beta cells, from non-diabetic donors and with WGBS data available from both cell types (Supplementary Tables 1 and 2). DNA methylation of 27.54 million cytosine-phosphate-guanine (CpG) sites was profiled and the distribution of methylation levels through different genomic regions visualized with dmrseq²⁵. Globally, methylation is bimodal, with most CpGs hypermethylated (right peaks) or unmethylated (left peaks), resulting in average methylation levels of 76.1% and 77.2% in alpha and beta cells, respectively (Fig. 1c). We also analysed the genome-wide methylation levels in relation to different genomic regions and CG density (Extended Data Fig. 1b and Fig. 1d,e). Introns, 3’ untranslated regions (UTRs) and intergenic regions exhibited the highest methylation levels, whereas promoters and 5’ UTRs had the lowest degree of methylation (Fig. 1d). Based on CG density, CpG islands had the lowest methylation level, while Open Sea and Shelves had the highest (Fig. 1e).

We then used RNA-seq data from alpha and beta cells (n = 16; Supplementary Table 1b) to explore whether methylation levels in different genomic regions associate with the expression level of genes in alpha and beta cells. Genes with <2 mean normalized counts were categorized as not expressed, and the remaining 24,730 and 24,726 genes in alpha and beta cells, respectively, were divided into three groups of similar size, categorized into low (0–33% quantile), medium (33–66% quantile) and high (66–100% quantile) expression. We found associations between the mean DNA methylation levels in different genomic regions and expression levels (Fig. 1f,g; Friedman rank-sum test). In promoters and 5’ UTR regions, lower methylation was associated with higher gene expression; while in intragenic regions, for example, exons, introns and 3’ UTR, lower methylation was associated with lower expression (Fig. 1f,g). Additionally, we found small significant differences between alpha and beta cells for the mean degree of methylation for different genomic regions linked to the four categories of expressed genes (Extended Data Fig. 1c–h).

DMRs in human alpha versus beta cells

To further dissect the epigenetic differences between alpha and beta cells, we used dmrseq²⁵ and WGBS data from seven donors not diagnosed with T2D and with the methylome available in both cell types (Supplementary Tables 1 and 2 and Extended Data Fig. 1i). On autosomal chromosomes, WGBS covered 25,907,683 CpGs. We found 22,544 differentially methylated regions (DMRs) annotated to 7,975 autosomal genes in alpha versus beta cells, when requiring three consecutive differentially methylated CpGs and false discovery rate (FDR) ≤ 5% (q < 0.05; Fig. 2a,b, Extended Data Fig. 1j and Supplementary Table 3a–c). These genes are enriched in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways including insulin secretion, cAMP signalling, glucagon signalling, calcium signalling, metabolic pathways and T2D mellitus (Fig. 2c and Supplementary Table 3d). The DMRs covered 3–379 CpGs (average 10 CpGs) and 9–10,752 base pairs (bp; mean length 637 bp). Extended Data Fig. 1k shows the annotation of the DMRs in relation to different genomic regions and CG density (Supplementary Table 3b,c). Interestingly, DMRs annotated to key alpha and beta cell genes^21,24,26,27, for example, PDX1, PLUT, INS, INS-IGF2, PCSK1, GCG, SLC2A2, MAFA, GATA6, SIX2 and GPR119, were among the most significant (Fig. 2d–i, Extended Data Fig. 1l–n and Supplementary Table 3b). Several genes have multiple DMRs, for example POU6F2 (n = 16), PLUT (n = 11), PCSK1 (n = 10), INS/INS-IGF2 (n = 7) and PDX1 (n = 3; Fig. 2d,e, Extended Data Figs. 2 and 3 and Supplementary Table 3b). The longest DMRs (≥6,104 bp) were annotated to, for example, GCG, PLUT, PDX, HADH, RXRG, PAX3, MAFA, ZC3H3 and KCNQ5, while DMRs annotated to PLUT, PDX1 and MAFA/ZC3H3 included most CpGs (≥212; Supplementary Table 3b, Fig. 2d,g and Extended Data Fig. 4a). We then compared the list of 7,975 genes with alpha versus beta cell DMRs identified in the current study with a list of genes presented in Supplementary Tables 9 and 10 in a similar study by Manduchi et al.²⁸. This showed that 468 of 706 (66%) genes, including GCG, INS, MAFA and PDX1, presented in their study exhibit DMRs also identified in our study (hypergeometric enrichment score (HES) = 2.07, P = 2.63 × 10⁻⁵⁸).

**Fig. 2: Genes important for islet cell function and T2D exhibit widespread DNA methylation differences in sorted human alpha versus beta cells.**

Epigenetics may regulate expression by controlling TF binding to the genome²⁹. Therefore, we investigated TF motifs and binding in identified DMRs. Using HOMER, we found enrichment of TF motifs, including NEUROD1, FOXA2, PDX1, FOXA3, FOXP1 and MAFB, in these DMRs (Fig. 2j and Supplementary Table 3e). Additionally, we present DMRs that overlap with sequences bound by the islet-specific TFs, FOXA2, MAFB, NKX2.2, NKX6.1 and PDX1 in Fig. 2k, and Supplementary Table 3f (enriched overlap, P ≤ 0.001)²⁴. Moreover, 270 DMRs, annotated to for example, CREB5, GLP1R, HDAC9, OPRD1 and SYT13, were bound by all five TFs.

Next, since genetics affects T2D, especially via islet dysfunction², and because we previously demonstrated interactions between genetics and epigenetics^30,31, we studied the overlap between genes linked to T2D-asociated SNPs² and the 7,975 genes with DMRs presented in Supplementary Table 3. Interestingly, 381 of the 849 (45%) T2D candidate genes also have DMRs in alpha versus beta cells (Fig. 2l and Supplementary Table 3g, HES = 1.74, P = 2.63 × 10⁻³⁴). Additionally, 25 T2D-associated SNPs are located within DMRs annotated to for example, INS-IGF2, CDKN2B, PCSK1, GLP1R, MEG3 and KCNQ1 (Fig. 2m and Supplementary Table 3g). Notably, we found that 446 of 1,289 T2D SNPs² are in CpGs (Supplementary Table 3g). Together, this supports that T2D-risk alleles can affect DNA methylation, and potentially alter T2D risk, via effects on methylation³⁰. Additionally, 831 of the DMRs, for example, annotated to GCG, SLC2A2 and AKT1, overlap with 1,086 CpGs with differential islet methylation in T2D (Supplementary Table 3a)¹². Finally, we intersected our alpha versus beta cell DMRs with three-dimensional chromatin map data³². Here, 30 of 89 chromatin interaction points that differ between alpha and beta cells overlap with an alpha versus beta cell DMR, with 15 overlapping with more than one DMR (Supplementary Table 3h, overlap z-score = 8.3, P ≤ 0.001).

Overall, these data clearly show cell-specific differences in the islet epigenome, overlapping key TF binding sites²⁴, T2D-associated SNPs² and T2D-associated islet DNA methylation¹².

Epigenetic differences on sex chromosomes in alpha versus beta cells were analysed separately in males and females. In males, we analysed 1,298,421 CpGs on X and Y chromosomes. We identified 453 DMRs, annotated to 178 genes, on the X chromosome, but none on the Y chromosome (Supplementary Table 4). Some of the most significant DMRs on the male X chromosome are annotated to ARX, USP27X, PRKX, GPR119, POLA1, MPP1, DLG3, HDAC8 and KDM6A (Fig. 2n, Extended Data Fig. 4b,c and Supplementary Table 4b). The longest X-chromosome DMRs (≥2,685 bp) are annotated to NEXMIF, GPR119, HS6ST2 and DACH2, while DMRs annotated to BCOR, HCFC1 and GPR119 have most CpGs (Supplementary Table 4b). Additionally, we present X-chromosome DMRs that overlap with sequences bound by FOXA2, MAFB, NKX2.2, NKX6.1 and/or PDX1 in Extended Data Fig. 4d and Supplementary Table 4d (enriched overlap, P ≤ 0.001). As there were only two non-diabetic females with WGBS data available in alpha and beta cells, we could not identify X-chromosome DMRs in females.

DEGs in human alpha versus beta cells

Next, we studied gene expression in alpha and beta cells from 16 non-diabetic donors (Supplementary Table 1b). Using RNA-seq, we found 13,001 and 12,876 genes expressed in alpha and beta cells, respectively (≥20 mean normalized counts in ≥80% of samples, unique Ensembl gene ID; Supplementary Table 5a,b). Notably, 93% and 94% of the genes found to be expressed in alpha and beta cells in our dataset overlap with previously published work²⁷, after reanalysing that data using the same conditions as ours (Supplementary Table 5a,b). While 12,384 genes were expressed in both cell types, 617 and 492 transcripts were uniquely expressed in either alpha or beta cells (Fig. 3a and Supplementary Table 5a,b). Based on gene ontology (http://www.webgestalt.org/), transcripts expressed in alpha cells, but not beta cells, showed enrichment in, for example, cAMP-mediated signalling, regulation of ion transport, regulation of secretion, exocytosis, regulation of apoptotic process and cell proliferation (Fig. 3b and Supplementary Table 5c). The 492 transcripts expressed only in beta cells, showed enrichment for 12 biological processes, for example, gland development (including NKX3.1), regulation of hormone levels (including MAFA, PDX1, GLP1R and SLC2A2), synaptic signalling (including NPY and SYT1), cell–cell signalling (including GAD1 and GLP1R) and ion transport (including GLRA1; Fig. 3c and Supplementary Table 5d). In both alpha and beta cells, ~40% of the 20 most abundantly expressed genes are mitochondrially encoded (Fig. 3d,e).

**Fig. 3: The transcriptome of sorted human alpha and beta cells.**

Additionally, 8,434 protein-coding genes were DEGs in alpha versus beta cells (q < 0.05; Fig. 3f and Supplementary Table 5e). Notably, ~40% (3,502) of these DEGs also have DMRs (Supplementary Table 5f), supporting epigenetic regulation of cell-specific expression (see for example, Fig. 2d–i, Extended Data Fig. 1l–n and Fig. 3g–o). Additionally, the DMRs correlated negatively with the DEGs (Supplementary Table 5g). We proceeded to focus on the biological relevance of the most significant DEGs in alpha versus beta cells (q < 10 × 10⁻⁸; Supplementary Table 6a). Of these 3,618 DEGs, ~50% had higher expression in each cell type, and they were enriched in KEGG pathways including insulin secretion, T2D mellitus and circadian entrainment, as well as glucagon, cAMP, AMPK, FoxO and calcium signalling (Fig. 3p and Supplementary Table 6b–d). DEGs showing higher expression in alpha cells (q < 10 × 10⁻⁸) were enriched for ten pathways, for example, cAMP signalling, while DEGs showing higher expression in beta cells (q < 10 × 10⁻⁸) were enriched for pathways related to, for example, insulin secretion and maturity-onset diabetes of the young (Supplementary Table 6e,f). Among DEGs with q < 10 × 10⁻⁸ in alpha versus beta cells, 1,835 (51%) also have DMRs annotated to them (Supplementary Tables 3 and 6).

Next, we intersected these DEGs with the 849 GWAS T2D candidate genes² and 186 (22%) genes, including ADCY5, GLIS3, GLP1R, HNF4A and INS, were overlapping (Fig. 3q and Supplementary Table 6g, HES = 1.67, P = 1.07 × 10³¹).

Epigenetic editing reveals a causal role for DNA methylation in regulation of INS, TH and GCG expression

The cell-specific DNA methylation differences, which we found linked to large differences in expression of key alpha and beta cell genes, suggest that the methylome directly regulates expression and potentially hormone levels in individual islet cell types (Fig. 2d–i and Supplementary Table 3b). To test this, in human EndoC-βH1 beta cells, we used epigenetic editing with CRISPR–dCas9–DNMT3A or CRISPR–dCas9–TET1 (ref. ³³) together with guide RNAs (gRNAs) targeting three alpha versus beta cell DMRs annotated to INS, TH and GCG (Figs. 2e,g and 4a–f).

Fig. 4: Epigenetic editing shows a causative role for DNA methylation of INS, TH and GCG on gene expression in beta cells. — **Fig. 4: Epigenetic editing shows a causative role for DNA methylation of *INS*, TH and *GCG* on gene expression in beta cells.**

CRISPR–dCas9–DNMT3A-mediated editing, with the INS-DMR-gRNA targeting the INS DMR (Figs. 2e and 4a,b) and the TH-DMR-gRNA targeting a region annotated to TH (Fig. 4c,e), resulted in increased methylation of 22 and 13 CpG sites, respectively, as analysed by pyrosequencing and EPIC v2.0 methylation arrays (Fig. 4g–j). Notably, after editing, 5 CpGs near the INS-DMR-gRNA target location showed absolute increased methylation of 20–43%, representing 2.46- to 30-fold increases, and these sites may have a stronger impact on gene expression than the other sites (Fig. 4h). DNMT3A-mediated editing using INS-DMR-gRNA and TH-DMR-gRNA caused reduced INS and TH expression, respectively (Fig. 4k,l). The targeted region in TH is located very near a potential INS enhancer (Fig. 4a,c) but editing using the TH-DMR-gRNA did not alter INS expression (Fig. 4k). Importantly, DNMT3A-mediated editing using the INS-DMR-gRNA led to reduced insulin content in human EndoC-βH1 beta cells (Fig. 4m).

Next, we used CRISPR–dCas9–TET1 and a gRNA (GCG-DMR-gRNA) targeting a DMR located in the promoter of GCG, encoding glucagon (Figs. 2g and 4d). This led to decreased DNA methylation at two CpG sites analysed by pyrosequencing and EPIC v2.0 (Fig. 4n,o). Additionally, the ten-eleven translocation methylcytosine dioxygenase 1 (TET1)-based editing using the GCG-DMR-gRNA resulted in increased GCG expression and glucagon content in the EndoC-βH1 beta cells (Fig. 4p,q).

Importantly, to our knowledge, this is the first study where epigenetic editing reveals a causal role for DNA methylation on INS, TH and GCG expression in beta cells, supporting future development of epigenetic-based therapies for regulation of, for example, insulin.

Pre-T2D/T2D-associated DMRs in human alpha and beta cells

To explore the epigenetic component of T2D, we compared the methylome in alpha and beta cells from donors with pre-T2D/T2D, based on having a haemoglobin A1c (HbA1c) ≥ 6% (≥42 mmol mol⁻¹) and/or a T2D diagnosis, with cells from controls, including non-diabetic donors with HbA1c < 6% (Supplementary Table 1a,b). In alpha cells, we found 3,207 autosomal DMRs associated with pre-T2D/T2D (hereafter called T2D-associated; permutation P < 0.05, Fig. 5a and Supplementary Table 7a–c). These DMRs were annotated to 1,871 genes, including GCG, INS-IGF2 and PDX1, enriched in KEGG pathways, including circadian entrainment (Fig. 5b and Supplementary Table 7d). The DMRs covered 3–257 CpGs and 10–18,509 bp. For protein-coding genes, T2D-associated alpha cell DMRs with lowest P values were annotated to ZNF566, UNC93A, CROCCP2, TBCA, USP18 or SNRPN, and the longest DMRs (>5,025 bp) were annotated to TUBBP5 and FLG, with the one annotated to TUBBP5 also covering most CpGs (Fig. 5a and Supplementary Table 7b). Figure 5c–h presents some T2D-associated alpha cell DMRs annotated to genes previously linked to T2D and/or islet function, for example, SNRPN³⁴, SLC52A2/TMEM249 (ref. ³⁵), CHURC1 (ref. ³⁶) and TULP4 (ref. ³⁷) or encoding epigenetic enzymes, for example KDM1B and KDM3B. Using HOMER, we found enrichment of TF motifs, including HNF1B and OCT4, in the T2D-associated alpha cell DMRs (Fig. 5i and Supplementary Table 7e), and we identified DMRs that overlap with sequences bound by the islet-specific TFs FOXA2, MAFB, NKX2.2, NKX6.1 and PDX1 (Fig. 5j and Supplementary Table 7f; enriched overlap, P ≤ 0.004). Additionally, 103 of 849 (12%) T2D candidate genes discovered by GWAS² also have T2D-associated alpha cell DMRs (Fig. 5k and Supplementary Table 7g; HES = 7.34, P = 1.37 × 10⁻²³⁸). This represents 5.5% of the 1,871 genes annotated to T2D-associated alpha cell DMRs. The overlapping genes include GIP, GLIS3, GRB10, HDAC9, IGF2, IGF2BP3, KCNQ1, KDM4B, MEG3, TH and TCF7L2. The T2D-associated SNP rs883541 (ref. ²) lies within a DMR annotated to WIPI1 (Fig. 5l). Additionally, 28 T2D-associated alpha cell DMRs overlap with 32 CpGs previously found to be differentially methylated in islets from donors with T2D versus controls¹² (Supplementary Table 7a). T2D-associated differences in methylation of individual CpGs of GCG, INS, PDX1/PLUT and MAFB in alpha cells are shown in Fig. 5m and Extended Data Fig. 4e–g.

**Fig. 5: Key islet and T2D candidate genes exhibit pre-T2D/T2D-associated DMRs in human alpha cells.**

In the beta cells, we discovered 5,106 T2D-associated autosomal DMRs annotated to 3,020 genes, for example, INS and INS-IGF2 (permutation P < 0.05; Fig. 6a and Supplementary Table 8a–c). The 3,020 genes were enriched in KEGG pathways including cAMP, Wnt, calcium, PI3K-Akt, FoxO and AMPK signalling (Fig. 6b and Supplementary Table 8d). T2D was associated with lower methylation of 75.5% (3,857) of these DMRs. The DMRs covered 3–315 CpGs (average 10 CpGs) and 11–7,594 bp (mean length 662 bp). The longest DMRs (>3,587 bp) were annotated to, for example TUBBP5, LINC01597, MYO16, SHISAL2B, FAM242A, LOC101927078, OR5D16 and MIR4321, while a DMR annotated to MIR4321, AMH and JSRP1 has the most CpGs (315 loci; Fig. 6a and Supplementary Table 8b). Figure 6c–j presents some T2D-associated beta cell DMRs annotated to genes previously linked to T2D and/or islet function, including EPS8L1 (ref. ³⁸), VEGFA³⁹, NKX6.3 (ref. ⁴⁰), CTNND2 (ref. ⁴¹), RGS6 (ref. ⁴²), FEM1B⁴³, SEC16A⁴⁴ and SORBS1 (ref. ⁴⁵). Using HOMER, we found enrichment of TF motifs including FOXA3, FOXA2, HNF1B, OCT4, FOXA1, PAX7, FOXP1, BACH2 and PDX1 in T2D-associated beta cell DMRs (Fig. 6k and Supplementary Table 8e). Additionally, we present T2D-associated beta cell DMRs that overlap with sequences bound by the islet-specific TFs FOXA2, MAFB, NKX2.2, NKX6.1 and PDX1 (Fig. 6l and Supplementary Table 8f; enriched overlap, P ≤ 0.001). Further, 153 of 849 (18%) T2D candidate genes identified by GWAS² also have T2D-associated beta cell DMRs, including CDKN2A, GLIS3, INS, KCNQ1, NKX6-3 and LPL (Fig. 6m and Supplementary Table 8f; HES = 2.69, P = 3.13 × 10⁻⁸⁵). This represents 5.1% of the 3,020 genes annotated to T2D-associated beta cell DMRs. Three T2D SNPs² (rs362307/HTT, rs150015181/NKX6-3 and rs34245505/BMF) are located within T2D-associated beta cell DMRs (Fig. 6n and Supplementary Table 8g). Notably, 65 T2D-associated beta cell DMRs overlap 91 CpGs exhibiting differential islet methylation in T2D¹² (Supplementary Table 8a). These include DMRs annotated to FER, ASCL5, HDAC4, CIT, PLX2 and FGF8. Additionally, Fig. 6o–q presents T2D-associated methylation differences of individual CpGs of GCG, INS and PDX1/PLUT in beta cells.

**Fig. 6: Key islet and T2D genes exhibit pre-T2D/T2D-associated DMRs in human beta cells.**

Pre-T2D/T2D-associated DEGs in human alpha and beta cells

We next investigated the impact of pre-T2D/T2D on gene expression in alpha and beta cells. By comparing RNA-seq data of alpha cells from 7 pre-T2D/T2D donors with 15 controls (Supplementary Table 1a,b), we identified 345 protein-coding DEGs with P < 0.05 (Fig. 7a and Supplementary Table 9a). When integrating these data with our published RNA-seq data from whole islets of a large T2D case–control cohort²⁶, and a sorted alpha cell T2D case–control cohort⁴⁶, we found 11 and 3 overlapping DEGs, respectively, including GNAL (Extended Data Fig. 5a and Supplementary Table 9b,c). Additionally, 26 T2D-associated DEGs also have DMRs in alpha cells, for example, BACH1 (Supplementary Table 9a).

**Fig. 7: Functional follow-up of T2D-associated DEGs in human alpha and beta cells.**

To identify regulators of insulin secretion that may contribute to T2D, we compared RNA-seq data of beta cells from 7 pre-T2D/T2D donors with 15 controls (Supplementary Table 1a,b). This identified 573 protein-coding DEGs with P < 0.05, including ADCY9, FBLL1, ZNF431, ZSWIM9, GNAL, ONECUT2, SNX1 and TRAPPC13 with FDR < 10% (Fig. 7b,c and Supplementary Table 10a). Notably, 43 of the DEGs found in beta cells overlap with T2D-associated DEGs found in whole islets,²⁶ including GABRA2, SOCS1, SYT1, PAX5, CABLES1 and TBC1D4; and 90% of these went in the same direction (Extended Data Fig. 5a–c and Supplementary Table 10b). Moreover, 20 and 19 DEGs found in our beta cells overlap with T2D-associated beta cell DEGs found in studies by Walker et al. and Martínez-López et al., respectively (Supplementary Table 10c,d)^46,47. Additionally, 88 genes exhibited both T2D-associated DEGs and DMRs in beta cells (Supplementary Table 10e). For the top DEGs (Fig. 7c), we identified individual CpGs annotated to ADCY9, ZNF431, ZSWIM9, GNAL, ONECUT2, SNX1 and TRAPPC13 with altered methylation in beta cells from donors with pre-T2D/T2D versus controls (Extended Data Fig. 5d–j). In line with this, CpGs annotated to ADCY9 (n = 5), ZNF431 (n = 1), ONECUT2 (n = 2) and SNX1 (n = 1) show differential islet methylation in T2D¹², supporting epigenetic dysregulation of these genes (Supplementary Table 10a,f). Notably, three T2D-associated islet DEGs²⁶ were differentially expressed in both alpha and beta cells in this study—GNAL, NTN1 and PRKCH (Extended Data Fig. 5a,k and Supplementary Tables 9b and 10b).

Exploring and visualizing DNA methylation by a web-based tool, alpha-beta-methylome

We proceeded to develop a web-based tool, alpha-beta-methylome (https://alpha-beta-methylome.serve.scilifelab.se/app/alpha-beta-methylome/), a comprehensive open resource based on the human WGBS and RNA-seq data included in this study (Fig. 1a and Supplementary Table 1c), for investigators to explore and visualize the impact of age, sex and pre-T2D/T2D on autosomal DNA methylation and/or expression of any gene in alpha and beta cells, as well as differences between alpha and beta cells, with the option to select P-value cut-offs and DNA methylation sequencing coverage. More detailed information is available at https://alpha-beta-methylome.serve.scilifelab.se/app/alpha-beta-methylome/.

Functional follow-up identifies T2D-associated genes that impair beta cell function

We then explored whether any of the T2D-associated DEGs presented in Supplementary Tables 9 and 10 have a functional role in alpha or beta cells. Protein-coding DEGs with FDR < 10% were selected for functional investigations, resulting in eight DEGs in beta cells (Extended Data Fig. 6a). Among the selected T2D-associated DEGs, ADCY9, FBLL1, ZNF431 and ZSWIM9 were downregulated, while GNAL, ONECUT2, SNX1 and TRAPPC13 were upregulated in beta cells from donors with pre-T2D/T2D (Fig. 7b,c and Extended Data Fig. 6a).

A systematic PubMed search showed that little, or nothing, was known about these genes in relation to beta cell function, and GNAL, TRAPPC13, ZNF431, ZSWIM9 and FBLL1 had previously not been linked to insulin secretion or beta cell function. SNX1 deficiency increased cAMP and insulin secretion from MIN6B1 cells in response to a glucagon-like peptide-1 analogue⁴⁸. Reduced Onecut2 expression in rat beta cells led to increased granuphilin expression, linked to decreased insulin secretion⁴⁹. Adcy9-deficient insulinoma cells showed reduced glucose-stimulated cAMP response and insulin secretion⁵⁰.

To mimic the situation in pre-T2D/T2D, selected genes showing lower expression in beta cells from donors with pre-T2D/T2D, that is, ADCY9, FBLL1, ZNF431 and ZSWIM9, were silenced using siRNA transfection in both human islets, and rat INS-1 832/13 beta cells (hereafter called INS-1 beta cells; Fig. 7c–e). We chose this cell line rather than EndoC-βH1 as, in our experiments, INS-1 beta cells behaved more like primary human beta cells than the fetal human EndoC-βH1 in gene manipulation experiments (Extended Data Fig. 6b)¹². siRNA transfection resulted in a 40–80% reduction in mRNA expression compared to islets/beta cells transfected with a negative control siRNA (siNC; Fig. 7d,e). Unfortunately, the ZNF431-targeting siRNA in islets failed in silencing the expression, and Znf431 is not expressed in INS-1 beta cells. ZNF431 was hence not investigated further. Interestingly, silencing of ADCY9, FBLL1 or ZSWIM9 reduced glucose-stimulated insulin secretion (GSIS) in both human islets and INS-1 beta cells (Fig. 7f,g) compared to siNC, with ADCY9 deficiency showing the strongest effect. Both Adcy9-deficient and Zswim9-deficient INS-1 beta cells showed reduced insulin secretion in response to K⁺ stimulation relative to siNC (Fig. 7h), indicative of issues downstream of closing ATP-sensitive potassium channels in the insulin secretion pathway.

Next, the selected genes that exhibited higher beta cell expression from donors with pre-T2D/T2D, GNAL, ONECUT2, SNX1 and TRAPPC13, were overexpressed using lentiviral transduction in human islets and INS-1 beta cells (Fig. 7c,i,j). Interestingly, only overexpression of ONECUT2 (OE-ONECUT2) significantly reduced GSIS in human islets (Fig. 7k), while OE-ONECUT2 or overexpression of TRAPPC13 (OE-TRAPPC13) significantly reduced GSIS in INS-1 beta cells (Fig. 7l). It is worth mentioning that in INS-1 beta cells, OE-ONECUT2 caused a severe reduction in GSIS and elevated basal secretion, similar to what is observed in individuals with T2D⁵¹. These changes resulted in reduced fold change of insulin secretion, that is, secretion at high glucose divided by secretion at low glucose, for OE-ONECUT2 and OE-TRAPPC13 (Fig. 7m). Overexpression of neither candidate affected K⁺-stimulated insulin secretion in INS-1 beta cells (Fig. 7n), suggesting that OE-ONECUT2 and OE-TRAPPC13 impair insulin secretion through mechanisms acting proximal to closure of ATP-sensitive potassium channels, for example in mitochondrial function.

OE-ONECUT2 causes reduced mitochondrial activity and Adcy9 deficiency impairs stimulus–secretion coupling

Because manipulation of ADCY9 and ONECUT2 had the strongest effects on insulin secretion (Fig. 7d–m), we decided to dissect the molecular mechanisms of these perturbances in INS-1 beta cells. We confirmed the Adcy9 deficiency and OE-ONECUT2 at protein levels by western blot analysis (Extended Data Fig. 6c,d). To determine whether Adcy9 deficiency and/or OE-ONECUT2 affect mitochondrial metabolism, we measured oxygen consumption rates (OCRs) using the Seahorse analyser. OE-ONECUT2 reduced mitochondrial respiration with lower basal and glucose-stimulated respiration, ATP production-coupled respiration, proton leak, maximal mitochondrial respiration and spare respiratory capacity in INS-1 beta cells, versus GFP (Fig. 8a). Adcy9 deficiency did not affect the OCR (Extended Data Fig. 6e). In line, OE-ONECUT2 caused a small significant reduction in ATP5A in complex 5 of the electron transport chain (Extended Data Fig. 6f). Using PercevalHR to assess ATP:ADP in live single cells⁵², we found impaired cytosolic ATP levels in OE-ONECUT2 INS-1 beta cells (Fig. 8b), demonstrating reduced mitochondrial activity in these beta cells. These data support that OE-ONECUT2 dampens mitochondrial activity, while Adcy9 deficiency impairs stimulus–secretion coupling in beta cells. The latter fits well with the extensive literature on the role of cAMP in enhancing insulin granule exocytosis in beta cells⁵³.

Fig. 8: Dysregulation of ONECUT2 leads to perturbed mitochondrial function, reduced insulin secretion and dysregulation of many genes in beta cells. — **Fig. 8: Dysregulation of *ONECUT2* leads to perturbed mitochondrial function, reduced insulin secretion and dysregulation of many genes in beta cells.**

To further characterize the mechanisms underlying the dysfunction caused by ADCY9 deficiency and OE-ONECUT2, we performed global transcriptomic analyses in human islets. We found 57 and 114 DEGs in ADCY9-deficient and OE-ONECUT2 islets, respectively (q < 0.1; Supplementary Tables 11a and 12a). Interestingly, NR4A3 and PCSK2 were DEGs in both ADCY9-deficient islets and in T2D beta cells (Supplementary Tables 10a and 11a). In addition to ONECUT2, also SYNPO2, CFB, VEGFB and PER3 were DEGs in both OE-ONECUT2 islets and T2D beta cells (Supplementary Tables 10a and 12a). HDAC9 and AZIN1 are T2D-associated genes in GWAS², and DEGs in ADCY9-deficient (Supplementary Table 11a) and OE-ONECUT2 islets (Supplementary Table 12a), respectively. In line with the impaired ATP production in OE-ONECUT2 INS-1 beta cells, OE-ONECUT2 human islets exhibited reduced expression of genes involved in oxidative phosphorylation (NDUFA1, NDUFA7 and COX10; Extended Data Fig. 6g) and glycolysis (GPI, ALDOA, ENO1 and ENO2; Supplementary Table 12a). Using gene-set enrichment analysis (GSEA), we tested whether sets of related genes had altered expression in ADCY9-deficient or OE-ONECUT2 islets. GSEA yielded 3 and 50 gene sets with downregulated expression, and two and one gene sets with upregulated expression in ADCY9-deficient and OE-ONECUT2 islets, respectively (q < 0.05; Supplementary Tables 11b and 12b). Interestingly, in OE-ONECUT2 islets, downregulated gene sets include cellular response to carbohydrates and glucose, and regulation of peptide secretion and insulin secretion (Fig. 8c,d). Individual genes contributing to enrichment for ‘regulation of insulin secretion’ in OE-ONECUT2 islets are presented in Fig. 8d and Extended Data Fig. 6h. In ADCY9-deficient islets, upregulated gene sets were linked to apoptosis (Extended Data Fig. 6i and Supplementary Table 11b), and cAMP signalling and insulin secretion were among enriched KEGG pathways (Supplementary Table 11c).

Next, because ONECUT2 is a TF, we used weighted gene correlation network analysis (WGCNA)⁵⁴ on 114 DEGs from OE-ONECUT2 in human islets (q < 0.1; Supplementary Table 12a) to test whether ONECUT2 is part of a dysregulated network. WGCNA revealed a network with ONECUT2 and 107 other genes, where ONECUT2 had stronger connection to 30 DEGs (Fig. 8e and Supplementary Table 12c). Four genes (ONECUT2, SYNPO2, PER3 and VEGFB) were both in the WGCNA ONECUT2 network and among T2D-associated beta cell DEGs (Supplementary Table 10a).

We proceeded exploring Onecut2 in a rodent diabetes model, the GK rat. We recently demonstrated mitochondrial dysfunction in GK islets¹². In line with beta cells from donors with pre-T2D/T2D showing elevated ONECUT2 expression (Fig. 7c), islets from GK rats had increased Onecut2 expression versus non-diabetic Wistar rats (Fig. 8f). In line with previous studies, islet Ins1 expression was decreased (P_nominal= 0.12) and blood glucose increased in GK versus Wistar rats (Fig. 8g,h).

Finally, we showed that silencing ONECUT2 in EndoC-βH1 beta cells exposed to high glucose plus palmitate (HGP; 19 mM glucose plus 1 mM palmitate), mimicking a diabetogenic environment, could rescue the perturbed insulin secretion seen in beta cells exposed to HGP (Extended Data Fig. 6j,k).

Our data support ONECUT2 as a key transcription factor in beta cells, controlling expression of genes involved in mitochondrial function, glucose homeostasis and insulin secretion.

Discussion

Here, we provide a unique resource, alpha-beta-methylome (https://alpha-beta-methylome.serve.scilif elab.se/app/alpha-beta-methylome/), where the community can explore associations between T2D, age and sex, and cell-type specific methylation and gene expression in human alpha and beta cells. By comprehensive analyses, we discovered regulatory regions which were unmethylated in one cell type, while they were highly methylated in the other, near genes with central roles in alpha and/or beta cells, including INS/INS-IGF2, PDX1/PLUT1, MAFA, PCSK1, GCG and ARX. The cell-specific epigenetic pattern was clearly linked to gene expression, and half of genes with DMRs were DEGs in alpha versus beta cells. Intriguingly, by epigenetic editing using CRISPR–dCas–DNMT3A³³, we increased INS promoter methylation, causing decreased INS expression and insulin levels in beta cells, providing evidence that methylation directly regulates transcription of this important gene. By integrating beta cell methylomes and transcriptomes from people with or without T2D, with comprehensive functional analyses in human islets and beta cells, we discovered that ONECUT2 has a key role in beta cell function and T2D pathogenesis.

TFs establish and maintain alpha and beta cell identity. Dysregulation of TFs contributes to islet dysfunction and diabetes^55,56. By dissecting methylomes, we discovered DMRs with large methylation differences in alpha versus beta cells, annotated to such TFs. Methylation in these DMRs likely controls TF expression and thereby cell fate. For example, ARX, essential for alpha cells, is hypomethylated and highly expressed in alpha cells, while it is hypermethylated and non-expressed in beta cells. MAFA and PDX1, essential for beta cells, were hypomethylated and highly expressed in the beta cells. We found cell-specific DMRs for NEUROG3, KLF4 and PAX6, important for alpha and beta cells⁵⁶. Long non-coding RNAs control TFs in alpha and beta cells. We identified DMRs annotated to PLUT, regulating PDX1 and PAUPAR, regulating PAX6 (refs. ^57,58). We found cell-specific DMRs in HNF1A, HNF1B and HNF4A, TFs contributing to maturity-onset diabetes of the young, and in PAX5, an upregulated TF in islets from people with T2D²⁶. By using our web tool, alpha-beta-methylome, we found T2D-associated methylation changes annotated to these TFs and long non-coding RNAs. In line with this, we previously discovered hypermethylation of PDX1 in islets of donors with T2D^8,11. Additionally, DMRs identified in this study overlap with sequences bound by FOXA2, MAFB, NKX2.2, NKX6.1 and PDX1.

T2D-associated SNPs impact islet function². Previously, we discovered associations between genetics and methylation as well as the chromatin structure in human islets^30,31,59. Here, we found that numerous genes linked to T2D SNPs also have T2D-associated DMRs in alpha or beta cells, and/or show differential methylation between alpha and beta cells, for example, INS, TCF7L2 (top T2D gene)² and GLP1R (treatment target)⁶⁰, as well as TFs mentioned above. Additionally, T2D SNPs are located within DMRs, further supporting interactions between genetics and epigenetics in T2D. We also found that 45% and 22% of T2D-associated genes/loci identified by GWAS overlap the alpha versus beta cell DMRs and DEGs, respectively. The reason for the higher overlap with genes annotated to DMRs than to DEGs could be because DNA methylation is more stable than gene expression, where expression of some genes represents more of a ‘snapshot’ at the time samples were collected. However, this needs to be further investigated.

We add ONECUT2 ‘as a piece of the puzzle’ of genes causing hampered mitochondrial activity and reduced GSIS in islets/beta cells, contributing to T2D (Extended Data Fig. 6l)^12,26,61,62. When analysing ~300,000 beta cells per donor, ONECUT2 expression was increased ~2-fold in people with pre-T2D/T2D, and ONECUT2 expression was higher in beta versus alpha cells. OE-ONECUT2 in human islets and INS-1 beta cells, mimicking T2D, caused reduced GSIS, OCR, ATP production and expression of gene sets regulating glucose metabolism and insulin secretion. The latter includes reduced expression of GCK, which encodes glucokinase, the rate-limiting enzyme in glycolysis. This could be an important change, leading to reduced production of pyruvate to fuel the mitochondrial tricarboxylic acid cycle, oxidative phosphorylation and ATP production⁶³. Rodent models showed downregulated Onecut2 when overexpressing miR-29a/miR-29b/miR-29c⁶⁴, and links between insulin resistance and downregulated ductal Onecut2/ONECUT2 (ref. ⁶⁵), supporting differences between mice and men⁶⁶. In 10x Genomics’ scRNA-seq data from non-diabetics (5,000–10,000 islet cells), ONECUT2 is predominantly expressed in ductal cells and to a lesser degree in beta cells, with limited expression in other cell types⁶⁷. While our data strongly implicate ONECUT2 in T2D pathogenesis, one should keep in mind that the overexpression of ONECUT2 is stronger in our in vitro model than in T2D beta cells in vivo.

Interestingly, ONECUT2 and ONECUT1 have nearly identical DNA binding sites, but data from our study and published studies suggest different roles for these two TFs⁶⁸. Mutations and common variants in ONECUT1 contribute to neonatal diabetes and T2D, respectively, and evidence supports that ONECUT1 impacts pancreas development^69,70,71. However, ONECUT1 expression was not detected in beta cells in our study, nor in fetal or adult beta cells sorted by Blodgett et al.²⁷. Furthermore, there was a 10-fold higher expression of ONECUT2 in adult compared to fetal beta cells²⁷, and ONECUT1 showed very low expression compared to ONECUT2 in beta cells in our human islet single-cell RNA-seq data⁶⁷.

This study focused on dissecting cell-specific and pre-T2D/T2D-associated DNA methylation in alpha and beta cells. However, since we previously showed that age and sex impact DNA methylation, gene expression and insulin secretion in human islets^72,73, we included analyses of age and sex associations on methylation and gene expression in our web tool, alpha-beta-methylome. Interestingly, sex-associated methylation and gene expression differences previously found in islets of, for example, BCL11A⁷³, were also identified in beta cells by exploring alpha-beta-methylome. Bcl11a-silencing reduced GSIS in beta cells, mimicking lower BCL11A expression and insulin secretion found in islets from males⁷³. Additionally, age-associated beta cell methylation of, for example, KLF14, FHL2 and ZNF518B, was identified using our web tool. These genes also showed age-associated methylation in islets⁷². We also explored whether results generated by Manduchi et al.²⁸ could be validated by using our web tool and, for example, we found age-associated hypomethylation of specific CpG sites in beta cells of genes involved in insulin secretion, such as FFAR1, RAPGEF4, RAB11FIP5, SMAD2, MYO5A, PDX1 and PTPRN. It should be noted that while our web tool performs a linear model between age and DNA methylation of individual CpG sites in beta cells from donors with an age range between 36 and 81 years, Manduchi et al. measured the average methylation differences between their older (39–58 years) and younger (18–35 years) donors.

This study provides a valuable resource for islet research by identifying genomic regions where DNA methylation seems to regulate cell-specific gene expression in human alpha and beta cells. For example, we discovered a region annotated to the promoter of SLC2A2, encoding GLUT2, with hypomethylation in beta cells compared to alpha cells. In line with this, we found SLC2A2 expression in beta but not alpha cells. GLUT2 is an important glucose transporter in rodent islets⁷⁴, but its importance in human islets has been questioned⁷⁵. Nevertheless, Hattersley’s group found SLC2A2 mutations causing neonatal diabetes, and they proposed that GLUT2 affects insulin secretion in humans⁷⁶. The epigenetic and expression data of SLC2A2 in human beta cells in the present study together with data from our previous study, where we found reduced SLC2A2 expression in islets from donors with T2D and that SLC2A2 knockdown reduced insulin secretion in human islets²⁶, support a role for GLUT2 in mature beta cells.

We used different cut-offs for unexpressed genes in our study. We first used <2 mean normalized counts to categorize genes as not expressed, and we categorized the remaining genes into low, medium and high expression. In the second analysis, we used ≥20 mean normalized counts in ≥80% of samples for a gene to be considered expressed in alpha and beta cells. The reason for using different cut-offs was that in the second analysis we wanted to include expressed genes that could have a biological role in the cells, while in the first analysis our goal was to investigate the degree of DNA methylation in relation to four different levels of expression. However, one should keep in mind that the cut-offs are based on our ‘best guess’.

This study has strengths and limitations. By using WGBS, we covered most of the methylome, compared with DNA methylation arrays, covering ~5%. We provide alpha and beta cell methylomes and transcriptomes from the same donors. As the number of samples is limited, partly because MARIS requires many islets and WGBS is challenging, future studies should include additional donors. While this study provides new data, some cell-specific findings could be ‘validated’ in larger cohorts of human islets, for example sex and age associations described above^72,73, and T2D associations described in the results, for example, 65 T2D-associated beta cell DMRs overlap 91 CpGs exhibiting differential islet methylation in T2D¹², supporting their robustness. Functional validations and results from pathway analyses further support the validity of our data. Interestingly, 75.5% of the T2D-associated beta cell DMRs identified in this study were hypomethylated, which is in line with our previous study where 77% of T2D-associated methylation sites had lower methylation in whole islets from donors with T2D¹², and the recent study by Manduchi et al. supporting hypomethylation in beta cells from donors with T2D²⁸. Additionally, genes discovered to have alpha versus beta cell DMRs in our study significantly overlapped with the ones found by Manduchi et al., and both our study and theirs found enriched gene pathways affecting T2D and secretion. A limitation is that only a few T2D-associated alpha cell DEGs could be validated in two other studies^26,46. However, one needs to keep in mind that Walker et al. used a gentler cut-off for inclusion of genes compared to our study⁴⁶. Additionally, since T2D is a heterogeneous and polygenic disease, diverse mechanisms and different genes contribute to the disease in different groups of people^2,77. This may affect the reproducibility between some studies, despite technical soundness.

Together, we provide comprehensive cell-specific methylomes and transcriptomes in alpha and beta cells from people with or without T2D and a web tool allowing the exploration of epigenetic dysregulations contributing to impaired insulin secretion and potentially T2D.

Methods

Ethics statement

Written informed consent to donate organs for biomedical research was obtained from the pancreatic donors or their relatives, and all procedures were approved by the ethics committees at Uppsala University and Lund University (permit nos. 2007-05 and 2011-263) following the Helsinki declaration. Donors were not compensated for their participation. Animal experiments were performed with permission from the Animal Ethics Committee of Lund University (permit no. 5.8.18-04115/2021) in accordance with the legal requirements of the European Community (86/609/EEC).

Human pancreatic islets

Pancreases were obtained from multi-organ donors, and pancreatic islets were isolated by collagenase digestion and density gradient purification at the Nordic Network for Clinical Islet Transplantation in Uppsala, Sweden. The findings are based on inclusion of both sexes, determined by self-reporting and concordant genetic testing. Sex is considered as a covariate in genome-wide analyses as described below. Supplementary Table 1a presents the clinical characteristics for all 24 donors included in this study, while Supplementary Table 1b presents characteristics for the donors included in each individual analysis of this study. These analyses include (i) pairwise analyses, that is, comparing data in alpha versus beta cells, in donors not diagnosed with T2D and with data available for both alpha and beta cells, and (ii) comparing data in alpha or beta cells from donors with pre-T2D/T2D, based on HbA1c ≥ 6% (≥42 mmol mol⁻¹) and/or T2D diagnosis, with data in alpha or beta cells from controls, including donors with HbA1c < 6% (<42 mmol mol⁻¹). Additionally, Supplementary Table 1c presents the characteristics of the donors included in each possible analysis using the web-based tool alpha-beta-methylome (https://alpha-beta-methylome.serve.scilifelab.se/app/alpha-beta-methylome/), which also includes (iii) associations between age and DNA methylation or gene expression using linear regression models in alpha or beta cells from non-diabetic donors, and (iv) sex associations by comparing data in alpha or beta cells from male versus female non-diabetic donors.

MARIS staining and FACS sorting of human alpha and beta cells

MARIS²³ was used to sort alpha and beta cells from islets from 24 donors. Briefly, islets were dispersed and fixed and then stained with antibodies for 30 min with guinea pig anti-insulin (A0564, 10119708; 1:600 dilution, Dako) and mouse anti-glucagon (MAB1249, HSQ0416111; 1:200 dilution, R&D Systems). Cells were then washed twice in DEPC-treated PBS containing 10% BSA, 1% saponin and RNase inhibitor before secondary staining with donkey anti-guinea pig Alexa Fluor 647 (706-605-148, 129003; 1:400 dilution, Jackson ImmunoResearch) and donkey anti-mouse Alexa Fluor 488 (A-21202, 1644644; 1:400 dilution, Invitrogen). The sorting of alpha and beta cells was performed on an Aria Fusion (BD Biosciences), achieving purity of 92.0% ± 7.6% for alpha cells and 96.5% ± 4.0% for beta cells. We obtained 322,627 ± 73,055 alpha cells and 311,550 ± 61,250 beta cells from each donor. When the number of sorted cells from a donor was deemed sufficient, they were split into two portions for extraction of RNA and DNA, respectively. Sample purity was also determined by deconvolution according Loyfer et al.²⁰ using GCG and INS as marker genes for alpha and beta cells, respectively, and either the gene expression (purity of alpha cells = 99.3% ± 0.3% and beta cells = 99.5% ± 0.1%) or DNA methylation (purity of alpha cells = 94.2% ± 0.8% and beta cells = 99.1% ± 0.4%) data generated by RNA-seq or WGBS (see below). Purity for individual samples was included in Supplementary Table 1a.

RNA and DNA isolation from human alpha and beta cells

After sorting, the alpha and beta cells were pelleted by centrifugation at 3,000g for 6 min at 4 °C. Total RNA and DNA were then extracted from the cells using the RecoverAll Total Nucleic Acid Isolation kit (Ambion, Life Technologies) according to the recommended protocol from the manufacturer, except that we incubated the RNA samples in digestive buffer for 3 h at 50 °C and then for 15 min at 80 °C instead of 15 min at 50 °C and 15 min at 80 °C, according to the MARIS protocol. The quality and integrity of the RNA and DNA samples were analysed using the Agilent ScreenTape and Fragment Analyzer systems (Agilent Technologies). Next, RNA and DNA concentrations were measured by Qubit/Quant-iT assay (Thermo Fisher Scientific) to verify sufficient quantity and quality for library preparations.

RNA-seq

Sequencing libraries were generated from 1 µg RNA from alpha and beta cells using the TruSeq RNA Library Preparation Kit followed by RNA-seq on a NextSeq 500 (Illumina). For preparation and analysis of data, quality and adaptor trimming was done using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), and then Salmon was used for quantification of transcript expression⁷⁸. Quality control was performed using fastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and multiQC tools⁷⁹. Downstream analyses were done using R⁸⁰. The R package tximport⁸¹ was used to import transcript-level abundance, estimated counts and transcript lengths into R using Gencode annotation v32. Differential gene expression analysis was done using DESeq2 (ref. ⁸²) which applies Wald’s test. For the analysis of DEGs and linear models, we considered a transcript to be expressed in a particular cell type if it has at least 20 reads in 40% of the samples. Correction for age, sex, body mass index (BMI) and days in culture was included in the analyses. Gene symbols referring to human mRNA expression are capitalized and italicized, and mRNA expression is referred to as ‘gene expression’.

WGBS and data analysis

Genomic DNA (2.2–50 ng) was treated with sodium bisulfite using the EZ DNA Methylation-Gold D5005 kit (Zymo Research) according to the manufacturer’s protocol. Sequencing libraries were prepared using the SPlinted Ligation Adapter Tagging (SPLAT) protocol⁸³. Cluster generation and paired-end 150-bp read length sequencing were performed using S4 flow cells with a NovaSeq 6000 and v1.5 sequencing chemistry (150 cycles, Illumina). The number of reads, alignment statistics and sequencing depth are reported in Supplementary Table 2. The sequencing data were processed using R⁸⁰ and MultiQC⁷⁹. Paired-end reads were trimmed using Trim Galore⁸⁴. Genome indexing was done using Genome Reference Consortium Human Build 38 (GRCh38/hg38). Gene annotations were retrieved using the R package TxDb.Hsapiens.UCSC.hg38.knownGene (version 3.4.6. 2019). Sequencing reads were aligned, and count data were generated using Bismark⁸⁵. Extended Data Figs. 7 and 8 present heat maps and dendrograms of the WGBS samples, respectively.

To identify DMRs, we used dmrseq²⁵, which uses transformed methylation proportions, and fits a linear regression model using generalized least squares with a nested autoregressive correlated error structure. The dmrseq package ‘weighs’ samples based on coverage and methylation state, meaning a CpG with 10 of 10 reads methylated is not treated the same as a CpG with 50 of 50 reads methylated, that is, higher coverage allows for more certainty of the methylation state. For the DMR calling, we used the following settings: >2 coverage in 70% of samples, a minimum DMR length of three CpGs and ten permutations. The t-statistics from Wald’s test were used to evaluate the strength of the covariate’s effect within each region, and statistical significance was determined via permutations using a pooled null distribution. The permutations involved calculating an initial test statistic from the observed data, then repeatedly shuffling the data and recalculating the statistic to build a reference distribution. The final P value is the proportion of shuffled results that are as extreme or more extreme than the original statistic. Additionally, dmrseq’s cut-off for the scalar value that represents the absolute value for the cut-off of the single CpG coefficient that is used to discover candidate regions was set to 0.20 for the analysis of alpha versus beta cells and to 0.05 for the analysis of pre-T2D/T2D versus controls. DMR annotations to gene and CpG island regions were done using hg38 and annotatr⁸⁶. The analysis of DMRs on autosomal chromosomes included data from both males and females, while X-chromosome and Y-chromosome DMRs were analysed in males and females separately.

HOMER TF motif discovery

HOMER (v.4.11, http://homer.ucsd.edu/homer/motif/) was run with findMotifsGenome.pl, to search for enrichments of TF binding motifs in identified DMRs.

Culturing cell lines

The human EndoC-βH1 beta cell line (female origin, EndoCells) was seeded in Matrigel and fibronectin-coated (100 μg ml⁻¹ and 2 μg ml⁻¹, respectively; Sigma-Aldrich) culture vessels in DMEM containing 5.6 mM glucose, 2% BSA fraction V, 10 mM nicotinamide, 50 μM 2-mercaptoethanol, 5.5 μg ml⁻¹ transferrin, 6.7 ng ml⁻¹ sodium selenite, 100 units per ml penicillin and 100 μg ml⁻¹ streptomycin.

The rat INS-1 832/13 beta cell line (male origin, shared by C. Newgard, Duke Molecular Physiology Institute) was cultured in RPMI 1640 medium with 11.1 mM D-glucose supplemented with 10% FBS, 2 mM L-glutamine, 1 mM sodium pyruvate, 50 µM β-mercaptoethanol, 5 ml penicillin–streptomycin (10,000 U/10 mg ml⁻¹) and 10 mM Hepes (HyClone).

All cells were incubated in a humidified atmosphere with 5% CO₂ at 37 °C.

Epigenetic editing, pyrosequencing, MethylationEPIC v2.0 array analyses, real-time qPCR and insulin and glucagon ELISA

Three different sets of epigenetic editing experiments were done in human EndoC-βH1 beta cells, which were seeded (180,000 cells per well) in 48-well plates 48 h before transduction. Next, the cells were transduced with all-in-one lentiviral constructs³³ at a multiplicity of infection (MOI) of 10 in the growth medium without antibiotics (penicillin–streptomycin) for 6 h. The constructs contain inactivated Cas9 (dCas9) fused to the DNA methyltransferase DNMT3A, and either INS-DMR-gRNA, a gRNA (sequence TGAGACTATAAAGCCAGCGGGGG) targeting an INS DMR (Figs. 2e and 4a,b), or TH-DMR-gRNA, a gRNA (sequence GCAGCTGTACTGGTTCACGG) targeting TH (Fig. 4c) as well as a potential INS enhancer (Fig. 4a,c). In the third set of editing experiments, EndoC-βH1 beta cells were transduced with a construct containing GCG-DMR-gRNA, a gRNA (sequence CATGCGTGATTGAAAGTAGAGCG) targeting the DMR presented in Figs. 2g and 4d, dCas9 fused to TET1, an enzyme involved in demethylating DNA. The virus-containing medium was then removed and replaced with complete growth medium. Forty-eight hours after transduction, cells were transferred into 24-well plates, and after an additional 48 h, cells were put into selection medium, including 4 µg ml⁻¹ puromycin, for 7 days. The selection medium was renewed after 3 days. After puromycin selection, cells from the same well were collected for RNA and DNA extraction, and cells from different wells were collected for protein extraction.

DNA and RNA were then prepared from the EndoC-βH1 beta cells using the Allprep RNA/DNA Mini Kit (80204, QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. The DNA and RNA concentrations were measured using a NanoDrop (ND-1000; Thermo Fisher Scientific).

Next, we performed bisulfite conversion using 200 ng of DNA using the EpiTect Bisulfite Kit (59104, QIAGEN) according to the manufacturer´s instructions. Using the PyroMark Assay design software 2.0 (QIAGEN), the following pyrosequencing assays were designed: INS-forward primer GATTTTTTTATTTTAGGTTTTAATGGG, INS-reverse primer ACAAACCTACTTAATAACCTCTTCTAATA, INS-sequencing primer TTTATTTTAGGTTTTAATGGGT, TH-forward primer GGGGTGATTTTATTTAAGGAATTTATTTAG, TH-reverse primer CCTAACCCACCTAAACTTATCCTT, TH-sequencing primer ATTGTTTGTTTGAGGAG, GCG-forward primer GGATATGTATAAAATAGGGATGGTTATGG, GCG-reverse primer AAAAAAACAACTTAAAAATCCACCTTCTA and GCG-sequencing primer AGGGATGGTTATGGG. Pyrosequencing was then run on a PyroMark Q48 autoprep instrument (QIAGEN). Notably, several pyrosequencing assays covering the genomic regions targeted by and surrounding the three investigated gRNAs failed, potentially due to CG-rich regions. Subsequently, we were unable to analyse methylation of all CpG sites in these regions by using pyrosequencing.

We proceeded to use the Infinium MethylationEPIC v2.0 BeadChip array (Illumina) to study DNA methylation of CpG sites covered by EPIC v2.0 probes in the INS, TH and GCG loci in samples from the epigenetic editing experiments. These arrays were analysed at Eurofins, Denmark. DNA was treated with bisulfite using the EZ-96 DNA Methylation kit (Zymo Research). Preprocessing and quality control of the methylation data were performed using R Statistical Software version 4.3.3 (https://www.R-project.org/). Probes on the array that target SNPs, non-CpG sites, cross-reactive probes⁸⁷, Y-chromosome-specific probes, probes targeting single-nucleotide variants of common cancer driver mutations and probes with a detection P value > 0.01 in more than 10% of the samples were removed. The intensities from the remaining probes were then corrected for background and dye bias using the NOOB function from the R package minfi (version 1.48.0)⁸⁸, and intra-sample normalization performed with the BMIQ function included in the R package wateRmelon (version 2.8.0)⁸⁹. To minimize batch effects, all paired editing samples (control and gRNA) were placed on the same array. Paired t-tests were used to analyse the methylation data covered by EPIC v2.0 probes from 10 kb upstream of the TSS to 10 kb downstream of the gene end of INS, TH and GCG. Here, EPIC v2.0 array covered 56 sites for INS, 62 for TH and 14 for GCG. M values were used for all bioinformatic and statistical analyses, and they were then converted to beta values (1–100% methylation) for presenting the results.

cDNA was generated with the RevertAid First Strand synthesis kit (K1622, Thermo Fisher Scientific). Gene expression was analysed by real-time qPCR in triplicates on a 384-well plate with TaqMan assays (Thermo Fisher Scientific) for INS (HS00355773_m1), TH (Hs00165941_m1), GCG (Hs01031536_m1) and PPIA (Hs04194521_s1, endogenous control), in a QuantStudio 7 Flex Real Time PCR system (Thermo Fisher Scientific) under default cycling parameters. Data collection and most analysis for epigenetic editing experiments were not performed blinded to the conditions of the experiment because samples needed to be loaded on plates and discs in a certain order. However, EPIC array experiments performed by Eurofins were blinded. No data points were excluded from the analyses.

Total protein from wells with edited EndoC-βH1 beta cells was extracted by using 200 μl RIPA buffer: 0.1% sodium dodecyl sulfate (SDS), 150 nM NaCl, 1% Triton X-100, 50 mM Tris-Cl, pH 8 and ethylenediaminetetraacetic acid (EDTA)-free protease inhibitor (Roche). The protein amount/concentration was analysed using the BCA Protein Assay kit (Thermo Fisher Scientific). Insulin and glucagon levels in beta cells were then analysed by ELISA (insulin: 10-1113-01; glucagon: 10-1271-01, Mercodia). BCA and ELISA plates were read on a CLARIOstar microplate reader (BMG Labtech).

Gene silencing, overexpression and insulin secretion in human pancreatic islets

Handpicked human islets (100–200) were placed in culture dishes containing RPMI 1640 media (11879020, Thermo Fisher Scientific) with 5 mM glucose, 10% FBS and 200 mM L-glutamine. Human islets were then transfected in a final volume of 2.5 ml per dish containing serum free Opti-MEM, 6.25 μl of Lipofectamine RNAiMAX, 50 nM of Silencer Select Pre-Designed siRNA against ADCY9 (s1037), FBLL1 (s196099), ZNF431 (s46878) ZSWIM9 (s51624) or negative control no. 2 siRNA (siNC, 4390846; all from Thermo Fisher Scientific). In line with previous studies^12,26, a second transfection was performed 24 h after the first, and all functional experiments were performed 72 h after the first transfection. For overexpression experiments, lentiviral plasmids containing the human phosphoglycerate kinase 1 promoter in front of cDNA of human GNAL, ONECUT2, SNX1 or TRAPPC13, with and without His-tag, followed by an internal ribosomal entry site and the cDNA for GFP, were cloned by GenScript. Lentiviral vectors were produced by Lund University Cell and Gene Therapy core facility. Lentivirus conferring gene expression of only GFP was used as control. Around 100–200 islets were transduced with these lentiviruses for 72 h with a MOI of 5. Gene symbols referring to human mRNA expression are capitalized and italicized, whereas when referring to human protein levels, protein symbols are capitalized but not italicized.

For insulin secretion, six batches of 6–12 human islets per donor were pre-incubated for 30 min in KREBS buffer (2.5 mM CaCl₂, 4.7 mM KCl, 120 mM NaCl, 25 mM NaHCO₃, 1.2 mM KH₂PO₄, 1.2 mM MgSO₄ and 10 mM HEPES) with 1 mM glucose before stimulation for 1 h in KREBS with either 1 mM or 20 mM glucose (n = 3 for each condition). After glucose stimulation, islets were dissolved in 100 μl RIPA buffer (50 mM TRIS-HCl, 150 mM NaCl, 0.5 mM sodium deoxycholate, 2 mM EDTA, 50 mM NaF, 1% Triton-X, 0.1% SDS). Insulin was measured by ELISA (10-1113-01, Mercodia), and secreted insulin was normalized to the total insulin content in the islets.

Gene silencing and overexpression in INS-1 beta cells and EndoC-βH1 beta cells

The INS-1 832/13 beta cells were seeded (250,000 cells per well) in 24-well plates containing 1 ml medium without antibiotics a day before transfection. For gene silencing, cells were transfected the following day, in a total volume of 600 µl of serum free Opti-MEM and 1.5 µl of Lipofectamine RNAiMAX with 25 nM Silencer Select Pre-Designed siRNA against Adcy9 (s235414), Fbll1 (s170925) or Zswim9 (s1587760), or silencer select negative control no. 2 siRNA (4390846) as negative control. All reagents were from Thermo Fisher Scientific. Cells were assayed 72 h after transfection, when reaching 90–100% confluence. For overexpression experiments in INS-1 beta cells, the same lentiviral vectors as described for human islets were used. Cells (79,000 cells per well in 48-well plates) were transduced for 24 h with a MOI of 1.25 and all experiments were performed 72 h after initiation of transduction. EndoC-βH1 beta cells were transfected as described above with RHOT1-targeting siRNA (s30650) or the negative control.

Insulin secretion in INS-1 beta cells and EndoC-βH1 beta cells

Confluent plates of transfected or transduced INS-1 832/13 beta cells or EndoC-βH1 beta cells, the latter cultured overnight in low-glucose media before experiments, were washed twice carefully with pre-warmed Secretion Assay Buffer (SAB), pH 7.2 (1.16 mM MgSO₄, 4.7 mM KCl, 1.2 mM KH₂PO₄, 114 mM NaCl, 2.5 mM CaCl₂, 25.5 mM NaHCO₃, 20 mM HEPES and 0.2% bovine serum albumin) containing 2.8 mM glucose. Cells were then pre-incubated in fresh SAB with 2.8 mM glucose for 2 h. Next, the cells were stimulated for 1 h in SAB with 2.8 mM glucose, 16.7 mM glucose or 2.8 mM glucose with 35 mM KCl at 37 °C. Insulin levels were measured by ELISA (10-1145-01 (rat) or 10-1113-01 (human), Mercodia) and normalized to total protein or total insulin content in each well. Total protein from each well was extracted by using 100–200 μl RIPA buffer: 0.1% SDS, 150 nM NaCl, 1% Triton X-100, 50 mM Tris-Cl, pH 8 and EDTA-free protease inhibitor (Roche). The protein content was analysed by BCA assay (Thermo Fisher Scientific). Plates were read on a CLARIOstar microplate reader.

Real-time qPCR in human islets, INS-1 beta cells, rat islets and EndoC-βH1 beta cells

Total RNA was extracted with the miRNeasy isolation kit (QIAGEN) according to the manufacturer’s instructions. The equipment and reagents mentioned below were purchased from Thermo Fisher Scientific. RNA concentrations were measured using NanoDrop (ND-1000). cDNA was generated with the RevertAid First Strand cDNA synthesis kit. qPCR was performed in triplicates on 384-well plates in a QuantStudio 7 Flex Real Time PCR system under default cycling parameters. TaqMan assays were used to measure expression levels of human ADCY9 (Hs00181599_m1), human FBLL1 (Hs01584505_s1), ZNF431 (Hs00819968_m1), human ZSWIM9 (Hs00736775_m1), human ONECUT2 (Hs00191477_m1), human RHOT1 (Hs00430256_m1), human SNX1 (Hs00162052_m1), human TRAPPC13 (Hs01556088_m1), human GNAL (Hs00181836_m1), rat Adcy9 (Rn01424753_m1), rat Fbll1 (Rn06322963_s1), rat Zswim9 (Rn01753793_m1), rat Onecut2 (Rn01265320_m1), rat Snx1 (Rn01418446_m1), rat Trappc13 (Rn01493919_m1), rat Gnal (Rn01491898_m1) and rat Ins1 (Rn02121433_g1). Human PPIA (Hs04194521_s1) and human HPRT1 (Hs02800695_m1), as well as rat Ppia (Rn00690933_m1) and Hprt1 (Rn01527840_m1), were used as endogenous controls for normalizing mRNA expression. Data are presented as relative quantification, describing the difference in expression of the gene compared with a control group. Threshold levels of all Ct values were automatically set, and the gene expression levels were normalized using the geometric means of the two endogenous controls, PPIA and HPRT1. Relative expressions were calculated with the ΔΔCt method.

Western blot analysis

To extract protein, cells were first lysed in ice-cold RIPA buffer (50 mM Tris-HCl pH 7.4–7.6, 150 mM NaCl, 2 mM EDTA, 1% Triton X-100, 0.5% sodium deoxycholate and 0.1% SDS) containing protease inhibitor cocktail (Sigma-Aldrich). Lysates were spun at 11,000g for 3 min to pellet debris and protein concentration of the supernatant was measured by BCA assay (Thermo Fisher Scientific). Next, 10 µg of protein was mixed with 5x sample buffer containing 10% β-mercaptoethanol and boiled before electrophoretic separation on 4–15% TGX Stain-Free gels (Bio-Rad). The gels were then activated with ultraviolet light for 1 min to visualize total protein on the blotted membrane. Protein was transferred to PVDF membrane with a TransBlot Turbo Transfer System (Bio-Rad). Membranes were blocked with 5% milk and 1% BSA in a buffer consisting of 150 mM NaCl, 20 mM Tris-HCl, pH 7.5, and 0.1% Tween for 1 h. The blots were probed with antibodies against Anti-6X His-tag antibody [EPR20547] - ChIP grade (ab213204, 1000344-30, clone EPR20547; 1:500 dilution, Abcam), ADCY9/AC9 (ab191423, GR269903-5, clone EPR16188; 1:500 dilution, Abcam) or total OXPHOS rodent antibody cocktail (ab110413-MS604, lot 2101021674; 1:250 dilution, Abcam) and incubated overnight at 4 °C. Secondary antibodies were horseradish peroxidase-conjugated goat anti-rabbit (7074, lot 32; 1:10,000 dilution, Cell Signaling Technology) or goat anti-mouse (1706516, 1:2,000 dilution, Bio-Rad). Clarity Western ECL Substrate was used for visualization of proteins with a ChemiDoc XRS+ System (Bio-Rad). The signal intensity of each protein band was measured with Image Lab software (version 5.2.1; Bio-Rad) and normalized to the total amount of protein loaded in the lane.

OCR measurements

Mitochondrial OCRs were evaluated with an XFe24 extracellular flux analyser (Agilent). Details of this analysis are described in Supplementary File 1.

ATP:ADP ratio measurement in live single cells using PercevalHR

The ATP/ADP ratio was measured with the fluorescent biosensor PercevalHR. A total of 70,000 INS-1 beta cells were seeded on poly-D-lysine (1 mg ml⁻¹)-coated Lab-Tek chambered cover glass (Thermo Fisher Scientific), followed by transfection with Lipofectamine 3000 (Thermo Fisher Scientific) with 1 μg of PercevalHR plasmid DNA (Addgene ID: 21737) per well together with lentivirus transduction containing either the cDNA of GFP or ONECUT2. Seventy-two hours later, the cells were pre-incubated in experimental buffer (pH 7.4): 3.6 mM KCl, 1.3 mM CaCl₂, 0.5 mM MgSO₄, 0.5 mM Na₂HPO₄, 10 mM HEPES, 5 mM NaHCO₃ and 135 mM NaCl, supplemented with 2.8 mM glucose, for 90 min at 37 °C. Cover glass with adhered cells was mounted on the stage of a Nikon ECLIPSE Ti2 microscope (Bergman Labora) equipped with a confocal unit. PercevalHR was excited with laser light at 488 nm and emission was detected at 520 nm at 2.8mM glucose and after addition of 16.7 mM glucose and 5 μM oligomycin sequentially.

Gene expression microarray of ADCY9-deficient or ONECUT2-overexpressing islets

Total RNA was extracted from human islets after silencing ADCY9 using siADCY9 (n = 5 donors) or ONECUT2 lentivirus overexpression (OE-ONECUT2, n = 6 donors) using the miRNeasy isolation kit (QIAGEN) according to the manufacturer’s recommendations. Details of the analyses of the microarray data are described in Supplementary File 2.

Rat model

Diabetic GK rats¹², developed by selective breeding of Wistar rats, and control Wistar rats from Janvier labs in France were kept in standard controlled housing conditions; 21–22 °C, 55–65% humidity and 12-h light–dark cycle, and were given standard chow (SAFE A40) and water ad libitum. Animals were used at 11–13 weeks of age. Only male rats were included to reduce the number of animals used. Blood glucose levels were measured from the tail vein using a glucometer (ACCU-CHEK Aviva, Roche). Pancreatic islets were isolated by collagenase digestion and handpicked in cold Hank’s buffer with 1 mg ml⁻¹ bovine serum albumin. Islets (75–200) were washed in ice-cold PBS, lysed in Qiazol (QIAGEN), transferred to microcentrifuge tubes, and homogenized by vortexing tubes for 1 min before storage at −20 °C until RNA extraction using the MiRNeasy Mini Kit protocol (217004, QIAGEN). RNA concentration was measured using NanoDrop (ND-1000 Spectrophotometer).

Rescue experiment in EndoC-βH1 beta cells exposed to HGP

Details are described in Supplementary File 3.

Statistical methods and data collection

Clinical characteristics between individuals with pre-T2D/T2D and non-diabetic controls were analysed using two-sample, two-tailed t-tests. DMR calling in alpha versus beta cells was done using dmrseq²⁵ (details are found above) including donors with data available in both cell types. DMR calling in donors with pre-T2D/T2D versus controls in alpha and beta cells was done in a similar way; however, here the analyses were adjusted for age, sex, BMI and days in culture of islets before MARIS. DEGs in alpha versus beta cells were analysed in paired samples using DESeq2 (ref. ⁸²). DEGs in donors with pre-T2D/T2D versus controls in alpha and beta cells were identified in a similar way, and here the analyses were adjusted for age, sex, BMI and days in culture of islets. FDR analysis was used to account for multiple testing, and FDR below 5% and 10% (q < 0.05 and q < 0.1) were applied. No statistical methods were used to pre-determine sample sizes, but they were based on results from our previous study where we analysed DNA methylation and gene expression in whole human islets¹¹. T2D-associated differences in DNA methylation of individual CpGs were analysed using a non-parametric Wilcoxon rank-sum test. Student t-tests (paired and two-tailed) were used in silencing experiments comparing siRNAs of target genes with control (siNC cells), as well as the overexpression experiments in human islets and INS-1 beta cells. Student’s t-tests (unpaired, two-tailed) were used for analyses of GK versus Wistar rats. Paired analysis of variance was used for analyses of the siONECUT2/HGP rescue experiment in EndoC-βH1 beta cells, with post hoc tests based on paired t-tests. Tests were two-tailed unless stated otherwise.

To show if overlapped sets display statistically significant enrichment, we used gene-set enrichment hypergeometric tests for gene overlaps and region-based permutation tests for genomic region overlaps. Details of these analyses are described in Supplementary File 4.

While the non-parametric tests used do not require normal distribution, parametric tests were used for normally distributed data. For EPIC v2.0 array data, M values were used for bioinformatic and statistical analyses. The collection and analysis of genome-wide data were performed blinded to the conditions of the experiment. Data collection and analysis for the remaining experiments were not performed blind to the conditions of the experiments. No data points were excluded from analysis unless they were technical outliers.

Figure preparation

Graphs in Figs. 1–8 and Extended Data Figs. 1–8 were created using GraphPad Prism and arranged in CorelDraw version 24.3.05.571. Fig. 1a and Extended Data Fig. 6l were created in BioRender; Ofori, J. https://biorender.com/h6zumch and https://biorender.com/1w54ey4 (2026). Euler diagrams for Figs. 2k, 5j and 6l were created using eulerr⁹⁰. Fig. 8e was generated using Cytoscape (v.3.10.3).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The WGBS and RNA-seq data generated from human alpha and beta cells are deposited in Zenodo (https://doi.org/10.5281/zenodo.18656853)⁹¹, and in the LUDC repository (https://www.ludc.lu.se/resources/repository; accession nos. LUDC2025.08.1 for the alpha cell WGBS data, LUDC2025.08.3 for the beta cell WGBS data, LUDC2025.08.4 for the alpha cell RNA-seq data and LUDC2025.08.5 for the beta cell RNA-seq data). Data can be requested through the repository portal and individual-level data from the human pancreatic islets are not publicly available due to ethical and legal restrictions related to the Swedish Biobanks in Medical Care Act, the Personal Data Act and European Union’s General Data Protection Regulation and Data Protection Act. The human alpha and beta cell RNA-seq data used for validation are available in the Gene Expression Omnibus under accession number GSE67543. The gene expression microarray data from ADCY9-deficient or ONECUT2-overexpressing human islets are available in the Gene Expression Omnibus under accession numbers GSE319105 and GSE319109, respectively). alpha-beta-methylome, an open resource based on the WGBS and RNA-seq data included in this study, is available at https://alpha-beta-methylome.serve.scilifelab.se/app/alpha-beta-methylome/, and allows users to explore and visualize the impact of age, sex and pre-T2D/T2D on autosomal DNA methylation and/or expression of any gene in alpha and beta cells, as well as differences between alpha and beta cells. Source data are provided with this paper.

Code availability

The computer code used to generate the results described in ‘WGBS and data analysis’ and ‘Statistical methods and data collection’ is deposited in Zenodo (https://doi.org/10.5281/zenodo.18681937)⁹². Other details are available from the corresponding author on on request.

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Acknowledgements

We thank the Nordic Network for Clinical Islet Transplantation (JDRF award 31-2008-413) for providing material for the study; the Human Tissue Laboratory at LUDC; T. Dayeh for experimental support; O. Korsgren, Uppsala University for support with the islet isolation infrastructure; C. Wollheim, University of Geneva for valuable discussions; Lund University Cell and Gene Therapy core facility for the support in virus production; and the Bioinformatics and Expression Analysis (supported by the Board of Research at the Karolinska Institute and the Research Committee at Karolinska Hospital) for microarray analysis. WGBS was performed by the SNP&SEQ Technology Platform in Uppsala, which is part of the National Genomics Infrastructure Sweden and Science for Life Laboratory, supported by the Swedish Research Council and the Knut and Alice Wallenberg Foundation. Research grants supporting the study were obtained from the Kungliga Fysiografiska Sällskapet i Lund (the Birgit and Hellmuth Hertz Foundation, to J.K.O.), Novo Nordisk foundation (to C.L.), the Swedish Foundation for Strategic Research (IRC15-0067, to C.L. and L.E.), the Swedish Research Council (2016-02486, 2018-02567, 2019-01406, 2014-2775, 2018-02435, 2021-00628 and 2024-03597, to C.L.), the Region Skåne (Avtal om Läkarutbildning och Forskning (ALF), to C.L.), Strategic Research Area Exodiab (2009-1039, to C.L. and L.E.), the European Research Council (ERC-Paintbox, to C.L.), the Diabetes Foundation (to C.L. and L.E.), as well as Syskonen Svensson, Magnus Bergvall, Hjelt (to S.R.), Åke Wiberg, Påhlsson Foundations, Sigurd och Elsa Goljes memory Foundation (to S.R.) and Lars Hierta’s memory Foundation (to S.R.). The flow cytometry study was performed at the LUDC-Flow Cytometry Core Facility, supported by the Swedish Research Council, Strategic Research Area Exodiab (2009-1039), by the Swedish Foundation for Strategic Research (IRC15-0067), by Region Skåne/(ALF) and by the Infrastructure Grant of Lund University, 2018. This work was also supported by the SciLifeLab & Wallenberg Data Driven Life Science Program (KAW2024.0159, to C.L.).

Funding

Open access funding provided by Lund University.

Author information

These authors contributed equally: Jones K. Ofori, Sabrina Ruhrmann.
These authors jointly supervised this work: Karl Bacos, Charlotte Ling.

Authors and Affiliations

Epigenetics and Diabetes Unit, Department of Clinical Sciences in Malmö, Lund University, Scania University Hospital, Malmö, Sweden
Jones K. Ofori, Sabrina Ruhrmann, Axel Lindström, Melina Martin, Katja Kost, Josefine Jönsson, Tina Rönn, Karl Bacos & Charlotte Ling
SciLifeLab, Lund University, Lund, Sweden
Axel Lindström & Lena Eliasson
Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
Alexander Perfilyev
Exodiab, Lund University Diabetes Centre, Lund University, Malmö, Sweden
Alexandros Karagiannopoulos, Åsa Nilsson & Monika Dudenhöffer-Pfeifer
Department of Advanced Medical and Surgical Sciences, University of Campania ‘Luigi Vanvitelli’, Naples, Italy
Lucia Scisciola
Institute of Pediatric Rare Diseases, Florida State University, Tallahassee, FL, USA
Boris Kantor
University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
Marianne G. Rots
Islet Cell Exocytosis Unit, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund University, Malmö, Sweden
Anna Wendt & Lena Eliasson

Authors

Jones K. Ofori
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Sabrina Ruhrmann
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Axel Lindström
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Alexander Perfilyev
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Melina Martin
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Alexandros Karagiannopoulos
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Lucia Scisciola
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Katja Kost
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Josefine Jönsson
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Åsa Nilsson
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Boris Kantor
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Monika Dudenhöffer-Pfeifer
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Marianne G. Rots
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Anna Wendt
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Tina Rönn
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Lena Eliasson
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Karl Bacos
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Charlotte Ling
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Contributions

C.L. initiated the project. J.K.O., S.R., A.L., A.P., M.M., A.K., L.S., K.K., Å.N., M.D.-P., A.W., K.B. and C.L. performed experiments and/or analyses. J.J., T.R., B.K., M.R. and L.E. designed experiments and experimental tools, and interpreted data. J.K.O., K.B. and C.L. drafted the manuscript. J.K.O. performed functional work in human islets, rat islets and INS-1 beta cells. S.R. performed all epigenetic editing experiments and contributed to writing the manuscript. A.L. developed the web tool and pipeline for EPIC v2.0 data and performed bioinformatic analyses. A.P. developed bioinformatic pipelines and performed bioinformatic analyses. All authors designed some experiments and/or analyses of data. All authors read and edited the manuscript.

Corresponding author

Correspondence to Charlotte Ling.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Maria Golson, Guoqiang Gu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Revati Dewal, in collaboration with the Nature Metabolism team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Characterization of the DNA methylome in sorted human α- and β-cells data related to Figs. 1 and 2.

a, Gating strategy used in sorting α- and β-cells from human islets after staining with antibodies against glucagon and insulin. b, CpG-sites are mapped to Gene regions based on functional genome distribution, where the promoter is defined as the region between the transcription start site (TSS) and 1 kb upstream, and to CpG island regions based on CpG content. A CpG island is defined as a ≥ 200 bp stretch of DNA with a CG content of ≥50% and an observed CpG/expected CpG in excess of 0.6. Shores are 2,000 bp regions flanking CpG islands. Shelves are 2,000 bp regions outside the shores. The Open Sea is everything outside the shelves. c-h, Average DNA methylation levels in α- and β-cells in different gene regions; 1-5 kb from TSS, p = 8.36E-07 (low), p = 3.23E-04 (not) (c), Promoters, p = 2.72E-04 (medium), p = 9.73E-09 (low), p = 0.0036 (not) (d), 5’untranslated regions (UTRs), p = 3.83E-04 (medium), p = 3.41E-05 (low), p = 0.040 (not) (e), Exons, p = 5.82E-05 (high), p = 0.008 (medium), p = 4.20E-06 (low), p = 1.11E-08 (not) (f), Introns, p = 3.83E-05 (high), p = 2.10E-05 (low), p = 2.41E-06 (not) (g), and 3’UTRs, p = 2.17E-04 (high), p = 1.25E-04 (low), p = 1.08E-07 (not) (h) of not expressed genes and expressed genes divided into low-, medium-, and high-expressed in α- and β-cells (n = 7 (α-cells) and 11 (β-cells) donors), *p < 0.05, **p < 0.01, ***p < 0.001 based on two-tailed Student’s t-tests. Data are presented as mean ± SEM. i, Coverage for the whole genome bisulfite sequencing (WGBS) data used for analysis of DMRs between sorted α- and β-cells from the same human islets (n = 7 donors). j, Manhattan plot displaying the 22,544 DMRs between α- and β-cells at a false discovery rate (FDR) less than 5% (q < 0.05 based on dmrseq²⁵). k, Proportion of identified DMRs annotated to different gene and CpG island regions. l, m, Left; α- versus β-cell DMRs annotated to GATA6 (l) and SIX2 (m) in sorted α- and β-cell (n = 7 donors, dmrseq²⁵). Right; expression of respective gene in sorted α- versus β-cells (n = 16, ***q < 0.001, based on DESeq2⁸², presented as mean ± SEM, see Supplementary Table 6 for exact P values). Women and men are represented by filled and open circles, respectively. n, The top X-chromosome DMR (only males, n = 5) annotated to GPR119 (left) and GPR119 expression (right) in sorted α- and β-cell (n = 10 males, ***q = 2.93E-35 based on DESeq2⁸², presented as mean ± SEM). The DMRs are analyzed and visualized by dmrseq²⁵. In DMR plots presented in l-n, locations of the methylation loci are shown with black lines at the bottom, and average, smoothed lines based on the dmrseq analysis are shown in pink (β-cells) or blue (α-cells), including standard deviation. The shaded area shows the DMR. Annotations to CpG island regions and genes are shown below each graph. The p-/q-values for j and l-n were corrected for multiple testing.

Source data

Extended Data Fig. 2 α- versus β-cell DMRs in POU6F2 data related to Fig. 2.

a-p, 16 significant DMRs annotated to POU6F2 identified in sorted α- versus β-cells (n = 7 donors) at a false discovery rate (FDR) less than 5% (q < 0.05, corrected for multiple testing). In all the DMR plots: locations of methylation loci are shown with black lines at the bottom and average, smoothed lines are shown in pink (β-cells) or blue (α-cells), including standard deviation. The shaded area shows the DMR. Annotations to CpG island regions and exons, if applicable, are shown below each graph. The DMRs were analyzed and visualized by dmrseq²⁵.

Extended Data Fig. 3 α- versus β-cell DMRs in PDX1/PLUT, data related to Fig. 2.

a-j, Significant DMRs annotated to PDX1/PLUT identified in sorted α- versus β-cells (n = 7 donors) at a false discovery rate (FDR) less than 5% (q < 0.05, corrected for multiple testing). In all the DMR plots: locations of methylation loci are shown with black lines at the bottom and average, smoothed lines are shown in pink (β-cells) or blue (α-cells), including standard deviation. The shaded area shows the DMR. Annotations to CpG island regions are shown below each graph. The DMRs were analyzed and visualized by dmrseq²⁵.

Extended Data Fig. 4 DMRs, TF binding and DNA methylation of individual CpG sites in α- and β-cells, data related to Figs. 2 and 5.

a, The longest α- versus β-cell DMR with most CpG sites is annotated to MAFA/ZC3H3. b, c, Some of the most significant α- versus β-cell DMRs on the X chromosome in males (n = 5 donors) are annotated to USP27X (b) and PRKX (c). Expression of respective gene in sorted α- and β-cells is shown on the right (n = 10 donors, data presented as mean ± SEM, ***q < 0.001, based on DESeq2⁸²). In DMR plots presented in a-c, locations of the methylation loci are shown with black lines at the bottom, and average, smoothed lines based on the dmrseq analysis are shown in pink (β-cells) or blue (α-cells), including standard deviation. The shaded area shows the DMR. Annotations to CpG island regions and genes are shown below each graph. The p-/q-values for a-c were corrected for multiple testing. d, A Venn diagram and table showing DMRs in α- versus β-cells on the X chromosome in males overlapping with binding sites for five islet specific transcription factors: FOXA2, MAFB, NKX2.2, NKX6.1 and PDX1 (ref. ²⁴). Overlaps are enriched with p ≤ 0.001 based on region-based permutation tests. For individual p-values, see Supplementary Table 4D. e-g, pre-T2D/T2D-associated differences in the absolute DNA methylation level (%) of individual CpG sites annotated to INS (e), PDX1/PLUT (f), and MAFB (g) in α-cells. *p < 0.05, **p < 0.01 based on two-tailed Wilcoxon rank-sum test in pre-T2D/T2D (n = 3) versus non-diabetic controls (n = 7), and data are presented as mean ± SEM. For exact p-values, see Source data file for Extended Data Fig. 4. Women and men are represented by filled and open circles, respectively.

Source data

Extended Data Fig. 5 Epigenetic regulation of T2D candidates, data related to Fig. 7.

a, Venn diagram showing T2D-associated differentially expressed genes (DEGs) in human α- and β-cells overlapping with DEGs identified in whole human islets by Bacos et al.²⁶ b, c, Examples of T2D-associated DEGs found in β-cells (n = 15 non-diabetic donors and 7 donors with T2D) that overlap with T2D-associated DEGs found in human islets by Bacos et al.²⁶: GABRA2, SOCS1, SYT1, PAX5, CABLES1 and TBC1D4 (*p < 0.05, **p < 0.01 based on DESeq2⁸²). d-j pre-T2D/T2D-associated differences in the absolute DNA methylation level (%) of individual CpG sites annotated to ADCY9 (d), ZNF431 (e), ZSWIM9 (f), GNAL (g), ONECUT2 (h), SNX1 (i), and TRAPPC13 (j), respectively in β-cells. *p < 0.05, **p < 0.01 pre-T2D/T2D (n = 3 donors) versus non-diabetic controls (n = 11 donors) based on two-tailed Wilcoxon rank-sum test. k, GNAL, NTN1, and PRKCH are T2D-associated DEGs in both sorted α- and β-cells, as well as in whole islets from Bacos et al.²⁶. Data shown is for β-cells from pre-T2D/T2D (n = 7 donors) versus non-diabetic controls (n = 15 donors), *p < 0.05, ***p < 0.001. In b-k, women and men are represented by filled and open circles, respectively. Data are presented as mean ± SEM. For exact p-values, see Supp. Table 10 (panels b, c, and k) and Source data files (panels d-j).

Source data

Extended Data Fig. 6 Selection of T2D candidates, data related to Figs. 7 and 8.

a, Candidate gene selection for functional follow-up from RNA-seq data in sorted α- and β-cells from donors with pre-T2D/T2D versus controls. Flowchart shows the differentially expressed genes (DEGs) in pre-T2D/T2D versus non-diabetic controls in α- and β-cells, respectively, and selection of eight DEGs in β-cells for follow-up. b, Gene manipulation in EndoC-βH1 β-cells often gives different phenotypes compared to human islets and INS-1 β-cells. Figure shows that RHOT1 knockdown (left, ***p = 0.0002) resulted in increased glucose-stimulated insulin secretion (right, **p = 0.0041) in EndoC-βH1 β-cells, which should be compared to reduced insulin secretion seen in human islets and INS-1 β-cells treated similarly (Figs. 4f and 5b in previous publication¹²). n = 6 biological replicates and paired t-tests. c, Reduced ADCY9 protein levels in INS-1 β-cells after siRNA transfection, n = 4 biological replicates, *p = 0.037 versus negative control (siNC) based on one-tailed paired Student’s t-test. d, Increased ONECUT2 protein levels in INS-1 β-cells after lentiviral transduction, n = 6 biological replicates, **p = 0.0079 versus GFP (control) based on paired Student’s t-test. e, No significant effect on oxygen consumption rate in siAdcy9 INS-1 β-cells, versus siNC, n = 4 biological replicates, p > 0.05 based on paired Student’s t-test. f, Reduced ATP5A protein levels in OE-ONECUT2 INS-1 β-cells, n = 6 biological replicates, *p = 0.049 versus GFP based on paired Student’s t-test. g, Microarray showed decreased expression of NDUFA1 (**p = 0.0022), NDUFA7 (**p = 0.0022), and COX10 (**p = 0.0043) in OE-ONECUT2 islets (n = 6 biological replicates/donors) based on Mann-Whitney U-tests, presenting unlogged data. h, Expression of NRID1 (**p = 0.0022), PTPRN (**p = 0.0022), and SLC8B1 (**p = 0.0087), contributing to the enrichment of the down-regulated gene set ‘regulation of insulin secretion’ in GSEA of OE-ONECUT2 human islets versus GFP (Mann-Whitney U-tests, presenting unlogged data). n = 6 biological replicates/donors. i, emapplot visualizing GSEA results of transcriptome data in human islets transfected with siADCY9 versus siNC. n = 5 biological replicates and GSEA was corrected for multiple testing. For full data, see Supplementary Table 11. j, ONECUT2 expression, and k, insulin secretion, presented as fold change (secretion at 16.7 mM glucose divided by secretion at 2.8 mM glucose), when silencing ONECUT2 using siRNA (siONECUT2) versus siNC in EndoC-βH1 β-cells exposed to high glucose (19 mM glucose) plus palmitate (1 mM palmitate, HGP) or control media for 72 h. n = 6 biological replicates, data analyzed by one-way ANOVA (j, p = 0.0293, and k, p = 0.0108) with paired Student’s t-tests as post hoc. j, *p = 0.023: control+siNC versus HGP+siONECUT2, ***p = 4.38E-05: HGP+siNC versus HGP+siONECUT2, and k, *p = 0.017: control+siNC versus HGP+siNC, *p = 0.049: HGP+siNC versus HGP+siONECUT2. n.s.: Not significant. l, Cartoon summarizing key results from this study; created in BioRender; Ofori, J. https://BioRender.com/1w54ey4 (2026). Data in b-h and i-j are presented as mean ± SEM. Tests were two-tailed.

Source data

Extended Data Fig. 7 Heatmaps of the WGBS samples, data related to Figs. 2, 5 and 6.

Heatmaps showing correlations between all a, α-cell samples (n = 10 donors), b, β-cell samples (n = 14 donors), c, α- and β-cells samples from donors with T2D (n = 6 donors), and d, α- and β-cell samples from the non-diabetic controls (n = 18 donors) for the WGBS data.

Extended Data Fig. 8 Dendrogram of the WGBS samples, data related to Figs. 2, 5 and 6.

Dendrogram including all α- (n = 10 donors) and β-cell (n = 14 donors) samples from the WGBS.

Supplementary information

Supplementary Information (download PDF )

Supplementary Files 1–4.

Reporting Summary (download PDF )

Peer Review File (download PDF )

Supplementary Table 1 (download XLSX )

Characteristics of the human donors of pancreatic islets included in the study, where alpha and beta cells were isolated by MARIS and used for WGBS and RNA-seq.

Supplementary Table 2 (download XLSX )

Sequencing information and alignment statistics of the WGBS data of human alpha and beta cells.

Supplementary Table 3 (download XLSX )

DMRs in alpha versus beta cells isolated from human pancreatic islets of seven non-diabetic donors.

Supplementary Table 4 (download XLSX )

DMRs in human alpha versus beta cells in X and Y chromosomes (males only, n = 5).

Supplementary Table 5 (download XLSX )

RNA-seq data showing expressed genes in human alpha and beta cells requiring ≥20 mean normalized counts in ≥80% of the samples (n = 16).

Supplementary Table 6 (download XLSX )

DEGs in alpha versus beta cells sorted from islets of 16 non-diabetic human donors.

Supplementary Table 7 (download XLSX )

Pre-T2D/T2D-associated DMRs in human alpha cells from donors with pre-T2D/T2D (n = 3) and controls (n = 7).

Supplementary Table 8 (download XLSX )

Pre-T2D/T2D-associated DMRs in human beta cells from donors with pre-T2D/T2D (n = 3) and controls (n = 11).

Supplementary Table 9 (download XLSX )

Pre-T2D/T2D-associated DEGs in alpha cells from donors with pre-T2D/T2D (n = 7) and controls (n = 15).

Supplementary Table 10 (download XLSX )

Pre-T2D/T2D-associated DEGs in beta cells from donors with pre-T2D/T2D (n = 7) and controls (n = 15).

Supplementary Table 11 (download XLSX )

DEGs between ADCY9-deficient (siADCY9) and control (NC) human pancreatic islets from five non-diabetic donors.

Supplementary Table 12 (download XLSX )

DEGs in human pancreatic islets showing overexpression of ONECUT2 (OE-ONECUT2) and control (GFP) from six non-diabetic donors.

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Unprocessed western blots.

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Ofori, J.K., Ruhrmann, S., Lindström, A. et al. Cell-specific DNA methylation in human alpha and beta cells regulates gene expression in type 2 diabetes. Nat Metab 8, 957–980 (2026). https://doi.org/10.1038/s42255-026-01498-9

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Received: 19 August 2025
Accepted: 03 March 2026
Published: 24 April 2026
Version of record: 24 April 2026
Issue date: April 2026
DOI: https://doi.org/10.1038/s42255-026-01498-9

Subjects

Abstract

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Main

Results

WGBS and RNA-seq of human alpha and beta cells

Global methylome of human alpha and beta cells

DMRs in human alpha versus beta cells

DEGs in human alpha versus beta cells

Epigenetic editing reveals a causal role for DNA methylation in regulation of INS, TH and GCG expression

Pre-T2D/T2D-associated DMRs in human alpha and beta cells

Pre-T2D/T2D-associated DEGs in human alpha and beta cells

Exploring and visualizing DNA methylation by a web-based tool, alpha-beta-methylome

Functional follow-up identifies T2D-associated genes that impair beta cell function

OE-ONECUT2 causes reduced mitochondrial activity and Adcy9 deficiency impairs stimulus–secretion coupling

Discussion

Methods

Ethics statement

Human pancreatic islets

MARIS staining and FACS sorting of human alpha and beta cells

RNA and DNA isolation from human alpha and beta cells

RNA-seq

WGBS and data analysis

HOMER TF motif discovery

Culturing cell lines

Epigenetic editing, pyrosequencing, MethylationEPIC v2.0 array analyses, real-time qPCR and insulin and glucagon ELISA

Gene silencing, overexpression and insulin secretion in human pancreatic islets

Gene silencing and overexpression in INS-1 beta cells and EndoC-βH1 beta cells

Insulin secretion in INS-1 beta cells and EndoC-βH1 beta cells

Real-time qPCR in human islets, INS-1 beta cells, rat islets and EndoC-βH1 beta cells

Western blot analysis

OCR measurements

ATP:ADP ratio measurement in live single cells using PercevalHR

Gene expression microarray of ADCY9-deficient or ONECUT2-overexpressing islets

Rat model

Rescue experiment in EndoC-βH1 beta cells exposed to HGP

Statistical methods and data collection

Figure preparation

Reporting summary

Data availability

Code availability

References

Acknowledgements

Funding

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Competing interests

Peer review

Peer review information

Additional information

Extended data

Extended Data Fig. 1 Characterization of the DNA methylome in sorted human α- and β-cells data related to Figs. 1 and 2.

Extended Data Fig. 2 α- versus β-cell DMRs in POU6F2 data related to Fig. 2.

Extended Data Fig. 3 α- versus β-cell DMRs in PDX1/PLUT, data related to Fig. 2.

Extended Data Fig. 4 DMRs, TF binding and DNA methylation of individual CpG sites in α- and β-cells, data related to Figs. 2 and 5.

Extended Data Fig. 5 Epigenetic regulation of T2D candidates, data related to Fig. 7.

Extended Data Fig. 6 Selection of T2D candidates, data related to Figs. 7 and 8.

Extended Data Fig. 7 Heatmaps of the WGBS samples, data related to Figs. 2, 5 and 6.

Extended Data Fig. 8 Dendrogram of the WGBS samples, data related to Figs. 2, 5 and 6.

Supplementary information

Source data

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